HIGH story-expense-type-selection-mutual-exclusion-peer-mentor-003 5 pts

User Story

As a Peer Mentor (Likeperson)
I want to see a live calculation of my estimated reimbursement amount as I select expense types and enter details
So that I can verify the claim is correct before submitting and understand exactly how the reimbursement is calculated

Audience Summaries

Real-time expense calculation preview directly addresses a critical trust and accuracy gap for peer mentors navigating HLF's complex reimbursement rules — where 60–70% of registrations involve no reimbursement at all and the remainder are subject to strict, multi-variable policies. By surfacing accurate cost breakdowns at the point of entry, this feature eliminates costly rework caused by misunderstood entitlements and incorrect submissions. Displaying auto-approval eligibility before submission reduces coordinator queue volume and speeds up processing, delivering measurable savings in administrative overhead. The result is a system that builds peer mentor confidence, drives higher feature adoption, and strengthens the organisation's value proposition as a self-service, low-friction reimbursement platform.

This story requires coordinated delivery across frontend UI, rate configuration backend, and auto-approval rules logic — all of which must be aligned before development begins. The hard dependency on the expense type selection mutual-exclusion story must be resolved first, as the preview panel subscribes directly to type selection state. The 300ms performance criterion is an acceptance gate that requires QA to define a load testing approach early, particularly for low-powered mobile devices used in field settings. Accessibility requirements (ARIA live region announcements) add a dedicated testing scope for screen reader compatibility across iOS and Android.

Rate configuration data availability must be confirmed in all target environments before UAT begins, or preview accuracy cannot be validated against real organisational rates.

The preview panel is a reactive UI component that subscribes to form field changes — expense type selection, distance input, toll amounts — and triggers recalculation within 300ms without a full page reload. Rate data is fetched from the rate configuration repository, ideally cached at session start to avoid per-keystroke network calls, with a session-boundary refresh to pick up configuration changes. Business logic must conditionally apply rate-per-km, toll threshold, and flat parking calculations based on selected types, rendering a line-item breakdown for multi-type selections. Auto-approval threshold comparison drives conditional rendering of green or amber status badges.

Accessibility requires an aria-live='polite' region that announces updated totals without disrupting the user's current focus position. Edge cases include the empty-state placeholder, zero or missing distance values, simultaneous multi-type field edits, and stale rate data across sessions.

Acceptance Criteria

  • Given the peer mentor selects 'mileage' and enters a distance, when the distance field updates, then the preview panel recalculates and displays the reimbursement amount within 300 ms without a full page reload
  • Given the calculated amount is below the auto-approval threshold, when the preview renders, then a green indicator and label 'Auto-approved' are shown
  • Given the calculated amount exceeds the auto-approval threshold, when the preview renders, then an amber indicator and label 'Requires coordinator approval' are shown
  • Given multiple expense types are selected (e.g., mileage + parking), when any field updates, then the preview shows a line-item breakdown per type and a total
  • Given the organisation's rate configuration changes, when the preview is rendered on a new session, then the updated rates are used in the calculation
  • Given a screen reader is active, when the calculation updates, then the updated total is announced via a live region without interrupting the user's current focus

Business Value

Real-time calculation preview reduces submission errors caused by misunderstood reimbursement rules and gives peer mentors immediate confidence that their claim reflects actual entitlement. For HLF, where 60–70 percent of registrations involve no reimbursement at all and the rest have strict rules, transparent calculation builds trust in the system. Showing auto-approval eligibility at claim time also reduces coordinator workload by setting correct expectations before claims enter the queue.